Bor Kiat Ng | Chief Technology Officer & Senior Vice President, Future Systems,
SMRT Trains Ltd | Singapore

Bor Kiat Ng, Chief Technology Officer & Senior Vice President, Future Systems,, SMRT Trains Ltd

Ng Bor Kiat, as Chief Technology Officer, oversees the Technology Management Office and is responsible for developing and implementing SMRT’s technology investment strategy, as well as enhancing technology partnerships with external organisations.

As Senior Vice President, Future Systems, he is responsible for the development of new technology-based capabilities to improve and sustain high levels of rail reliability.

Prior to joining SMRT, Mr Ng was Director, Corporate Development at the Ministry of the Environment and Water Resources, with responsibility over all of its corporate functions. He has close to 30 years of management and engineering experience, with an extensive background in research and development, project and system management, maintenance operations and corporate functions. He has held numerous senior positions in his career including Director (Land Systems) at Defence Science and Technology Agency, as well as Chief Maintenance and Engineering Officer while on secondment to the Singapore Armed Forces and as consultant to the Temasek Defence Systems Institute of NUS.

Mr Ng was awarded the Defence Technology Prize (Team) in 1997 and Public Administration Medal (PPA) Silver in 2007.

Mr Ng holds a Master of Science from Cranfield Institute of Technology, UK, and a Bachelor  of Engineering (Honours) in Mechanical Engineering from Tokyo Institute of Technology, Japan. He has attended the Programme for Management Development at Harvard Business School and the Stanford-NUS Executive Programme.

Appearances:



Day Two @ 14:40

Digitalisation of the railways – SMRT’s development on future systems

  •     Moving from preventive to predictive approach – achieving cost-savings over routine or time-based preventive maintenance  
  •     With infrastructure constantly ageing, what strategies can be adopted to achieve maintenance optimization? 
  •     How can sensor technology and big data analytics be utilised effectively to reduce maintenance costs? 

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